Abstract
In this chapter, we present a parametric method for generating a super-resolution image from a sequence consisting of blurred and noisy observations. The high resolution image is modeled as a Markov random field (MRF) and a maximum a posteriori (MAP) estimation technique is used for super-resolution restoration. Unlike other super-resolution imaging methods, the proposed technique does not require sub-pixel registration of given observations. A simple gradient descent method is used to optimize the cost. The discontinuities in the intensity process can be preserved by introducing suitable line processes. Superiority of this technique to standard methods of image interpolation is illustrated. The motivation for using blur as a cue is also explained.
On leave from Dept. of Electronics, Cochin University of Science and Technology, Cochin - 682 022. India.
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References
H. Ur and D. Gross, “Improved resolution from sub-pixel shifted pictures,” CVGIP:Graphical Models and Image Processing, vol. 54, pp. 181–186, March 1992.
S. Chaudhuri and A. N. Rajagopalan, Depth from defocused images: A real aperture imaging approach, Springer-Verlag, New York, 1999.
J. Besag, “Spatial interaction and the statistical analysis of lattice systems,” Journal of Royal Statistical Society, Series B, vol. 36, pp. 192–236, 1974.
S. Geman and D. Geman, “Stochastic relaxation, Gibbs distribution and the Bayesian restoration of image,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 6, no. 6, pp. 721–741, 1984.
Andrew Blake and Andrew Zisserman, Visual Reconstruction, MIT Press, 1987.
R. Kindermann and J. L. Snell, Markov Random Fields and their applications, American Mathematical Society, Providence, RI, 1980.
H. Derin and H. Elliot, “Modeling and segmentation of noisy and textured images using gibbs random fields,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 9, pp. 3–55, 1987.
Richard C. Dubes and Anil K. Jain, “Random field models in image analysis,” Journal of Applied Statistics, vol. 16, no. 2, pp. 131–164, 1989.
S. Z. Li, Markov Random Field Modelling in Computer Vision, Springer-Verlag, Tokyo, 1995.
J. L. Marroquin, Probabilistic solution of inverse problems, Ph.D. thesis, MIT AI Lab, 1985.
Michael Elad and Arie Feuer, “Restoration of a single super-resolution image from several blurred, noisy and undersampled measured images,” IEEE Trans. on Image Processing, vol. 6, no. 12, pp. 1646–1658, December 1997.
M. Elad and A. Feuer, “Restoration of a single super-resolution image from several blurred, noisy and undersampled measured images,” Tech. Rep. EE Pub No. 967, Dept. of Electrical Engg, Technion, Israel Instt. of Technology, May 1995.
A. N. Rajagopalan and S. Chaudhuri, “Space-variant approaches to recovery of depth from defocused images,” Computer Vision and Image Understanding, vol. 68, no. 3, pp. 309–329, Dec. 1997.
R. R. Schultz and R. L. Stevenson, “A Bayesian approach to image expansion for improved definition,” IEEE Trans. on Image Processing, vol. 3, no. 3, pp. 233–242, May 1994.
S. Krishnamachari and Rama Chellappa, “Multiresolution gaussmarkov random field models for texture segmentation,” IEEE Trans. on Image Processing, vol. 6, no. 2, pp. 251–266, February 1997.
Deepu Rajan and Subhasis Chaudhuri, “imultaneous estimation of super-resolved intensity and depth maps from low resolution defocused observations of a scene,” in Proc. of International Conf. on Compuner Vision, Vancouver, Canada, 2001.
Deepu Rajan, Some new approaches to generation of super-resolution images, Ph.D. thesis, School of Biomedical Engineering, Indian Institute of Technology, Bombay, 2001.
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© 2002 Kluwer Academic Publishers
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Rajan, D., Chaudhuri, S. (2002). Super-Resolution Imaging Using Blur as a Cue. In: Chaudhuri, S. (eds) Super-Resolution Imaging. The International Series in Engineering and Computer Science, vol 632. Springer, Boston, MA. https://doi.org/10.1007/0-306-47004-7_5
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DOI: https://doi.org/10.1007/0-306-47004-7_5
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